SOTAVerified

Emotional Intelligence

Emotional Intelligence (EI) is a measure of "The ability to monitor one’s own and others’ feelings, to discriminate among them, and to use this information to guide one’s thinking and action." (Salovey and Mayer, 1990). EI is further broken down into four branches: perceiving, using, understanding and managing emotions (Mayer & Salovey, 1997). Of particular relevance to language models that operate exclusively in the text modality is emotional understanding (EU). This is defined as the ability to interpret and analyse the language of emotions, to comprehend complex emotional states, and understand how these emotions can influence behaviour and decision-making.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–25 of 77 papers

TitleStatusHype
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective TasksCode3
RLVER: Reinforcement Learning with Verifiable Emotion Rewards for Empathetic AgentsCode3
Scaling Language Models: Methods, Analysis & Insights from Training GopherCode2
EmoBench: Evaluating the Emotional Intelligence of Large Language ModelsCode2
EQ-Bench: An Emotional Intelligence Benchmark for Large Language ModelsCode2
REALTALK: A 21-Day Real-World Dataset for Long-Term ConversationCode1
EmoLLM: Multimodal Emotional Understanding Meets Large Language ModelsCode1
SAGE: Steering and Refining Dialog Generation with State-Action AugmentationCode1
NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional StimuliCode1
Real-Time Emotion Classification Using EEG Data Stream in E-Learning ContextsCode1
Are there intelligent Turing machines?Code0
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across familiesCode0
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese MetaphorsCode0
Divergences between Language Models and Human BrainsCode0
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed DialoguesCode0
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational AgentsCode0
Emotion Twenty Questions Dialog System for Lexical Emotional IntelligenceCode0
Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General IntelligenceCode0
Investigating Emotion-Color Association in Deep Neural NetworksCode0
CuentosIE: can a chatbot about "tales with a message" help to teach emotional intelligence?—0
Controllability Analysis of Functional Brain Networks—0
Large Language Models Understand and Can be Enhanced by Emotional Stimuli—0
Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OpenAI gpt-4-0613EQ-Bench Score62.52—Unverified
2migtissera/SynthIA-70B-v1.5EQ-Bench Score54.83—Unverified
3OpenAI gpt-4-0314EQ-Bench Score53.39—Unverified
4Qwen/Qwen-72B-ChatEQ-Bench Score52.44—Unverified
5Anthropic Claude2EQ-Bench Score52.14—Unverified
6meta-llama/Llama-2-70b-chat-hfEQ-Bench Score51.56—Unverified
701-ai/Yi-34B-ChatEQ-Bench Score51.03—Unverified
8OpenAI gpt-3.5-0613EQ-Bench Score49.17—Unverified
9OpenAI gpt-3.5-turbo-0301EQ-Bench Score47.61—Unverified
10Open-Orca/Mistral-7B-OpenOrcaEQ-Bench Score44.4—Unverified